Abstract

MADRAS is an acoustic monitoring system that features the automatic recognition of noisesources. It is based on a tree classifier able to use different classification techniques like signal processing, morphology, fractional analysis, and neural networking. A very high accuracy of recognition is achieved due to the optimization of the choice of real‐time technique applied and to the intrinsic performance of each algorithm. MADRAS can learn the characteristics of new sources and also use a database of previously processed cases and typical events. The paper presents the results of multiple source recognition in a complex acoustic environment. Implemented on a real time PC‐based measuringanalyzer called Symphonie, MADRAS has been able to distinguish all noisesources. Acquired data may then be processed to obtain the energy contribution of each source and a list of the frequency of each appearance. Simulations may be viewed and modified.